Abstract

The increasing volume, variety, and velocity of health-related data generated from diverse sources—such as electronic health records (EHRs), wearable devices, public health databases, and clinical trials—has created an urgent need for effective integration and interpretation frameworks. Traditional data integration approaches often struggle to reconcile heterogeneous structures, semantics, and formats, resulting in fragmented insights and suboptimal healthcare outcomes. To address this challenge, this study presents the development of a comprehensive Knowledge Graph (KG) designed to unify and semantically enrich health data from multiple sources. The proposed Knowledge Graph leverages ontologies and semantic web technologies to create a scalable and interoperable framework for integrating structured and unstructured health data. Through entity extraction, relationship mapping, and schema alignment, the KG captures complex interconnections among patient data, medical concepts, treatments, diagnoses, and outcomes. We utilize natural language processing (NLP) techniques to transform unstructured text from clinical notes and research articles into structured knowledge, while standardized vocabularies such as SNOMED CT, ICD-10, and LOINC are employed to ensure semantic consistency. The architecture of the Knowledge Graph incorporates a hybrid model that combines rule-based reasoning with machine learning algorithms for knowledge inference and data validation. Real-world case studies demonstrate how the system enables advanced querying, patient stratification, and disease progression modeling, offering clinicians and researchers a unified view of patient histories and public health trends. Furthermore, the integration of temporal and geospatial dimensions enhances the capacity to monitor epidemics, identify risk factors, and support precision medicine. This research highlights the importance of semantic interoperability, data provenance, and real-time updating mechanisms in the design of robust health data infrastructures. By fostering a holistic understanding of multi-source health data, the Knowledge Graph not only streamlines clinical decision-making but also opens new avenues for population health management, biomedical discovery, and policy formulation. Future work will explore the integration of privacy-preserving technologies and federated learning to ensure data security and ethical compliance.